Application of a Modular Simulation Approach: Optimizations from Combustion to Vehicle Management

Gaballo, Maria Rosaria; Giodice, Maria; Diano, Alberto; Fersini, Fabio; Miccolis, Francesco; Mannal, Soenke; Motz, Stefan

doi:10.4271/2015-24-2505

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…In the last decade, the New European Driving Cycle (NEDC) has been the main target cycle for emission calibration. Later, Gaballo et al 16 proposed the Worldwide harmonized Light vehicles Test Cycle (WLTC) which allows to observe also the transient effects on the emissions. More recently, with the latest Euro 6d regulations, RDE cycles have been introduced in order to have a more realistic emission cycle.…”

Section: Introductionmentioning

confidence: 99%

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Investigation of an efficient model-based fuel economy optimization methodology for diesel engines focusing on real world driving emission cycles

Karabiyik

Öncü

Samur

2022

International Journal of Engine Research

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Together with stringent emission standards, meeting fuel consumption, and performance targets have made engine calibration process complicated, lengthy, and costly. For this reason, it is crucial to develop an efficient methodology for the optimization of a diesel engine calibration. In this study, a novel automated fine tuning methodology using genetic algorithms and data driven models is proposed. Fuel consumption of a light commercial vehicle with 2.0-L diesel engine was optimized for a predefined Real Driving Emissions (RDE) cycle. During the optimization, tail pipe emission limits were maintained by taking the aftertreatment system efficiency into account. To this end, a representative engine control unit, internal combustion engine, and Selective Catalyst Reduction (SCR) system models were built by using empirical data and neural networks. Moreover, the effect of choosing different tailored optimization points for a predefined route was investigated by comparison of frequency and fuel consumption based weighting. The cost function, related to the optimization problem, was defined based on fuel consumption, performance, and mechanical limits of the engine, and in accordance with tailpipe emission limits defined by Euro 6d-Temp regulations. Compared to previous model-based calibration studies, tailpipe emissions, and regulation limits have been incorporated in this study in order to obtain more realistic calibrations. After testing different genetic algorithm configurations, the fuel efficiency over the predefined route was improved by 3.07% without violating any of the limits while the smoothness of the calibration maps was also preserved. This route specific calibration methodology is believed to have the potential to increase fuel economy in real world applications such as daily commuting routes or repetitive driving patterns for instance, operation vehicle use cases in factories, airports, and construction areas. As for further development, this can be achieved by running simulations and the optimization on cloud and updating the calibration remotely based on the selected route before the trip.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigation of an efficient model-based fuel economy optimization methodology for diesel engines focusing on real world driving emission cycles

Karabiyik

Öncü

Samur

2022

International Journal of Engine Research

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“…For example, qualitative 'Face Validity' is achieved by an expert user who can 'validate' the model based on their knowledge of how the real system operates under given conditions. [15][16][17][18][19][20][21] More advanced quantitative statistical techniques can range from evaluation of percentage error and root mean square errors (RMSs) between simulation and experimental results for given characteristics, [22][23][24][25][26] through to more comprehensive evaluation of signal correlation such as use of the 'R-Squared' method as shown by Wang et al 27 and some methods which use the frequency domain in their analyses to capture high frequency transient phenomena. 28,29 The single metric approaches have also been extended to multi-step validation procedures, for example, in the approach of Klemmer et al 30 where a two stage method for validation of a vehicle model based on a signal correlation, a correlation rating and a validation coverage rating was presented.…”

Section: Introductionmentioning

confidence: 99%

Multi-fidelity validation algorithm for next generation hybrid-electric vehicle system design

Stevens

Early

Cunningham

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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With the evolution of increasingly complex hybrid-electric vehicle powertrains, the process of creating validated system models has become progressively more difficult to achieve. Increasing levels of confidence in how instantaneous vehicle energy states are captured is needed to take full advantage of the fuel consumption and emissions reduction potential of the vehicle to move towards more sustainable transportation systems. While many strategies for model validation exist, the majority rely on ascertaining comparisons with global system characteristics, for instance, total fuel consumption over a fixed driving event. However, these methods do not necessarily account for the rapidly fluctuating energy states which need to be understood to optimise the vehicle’s energy management strategy. The current work proposes a new validation approach which captures these instantaneous characteristics taking advantage of the high signal sampling rates available from modern data acquisition equipment rather than relying on drive cycle average or cumulative global behaviours. The method proposed provides a holistic view of the behaviours demonstrated by the vehicle model and identifies regions of poor system validation targeting areas for further model refinement. The algorithm is demonstrated on a new post-transmission, parallel mild-hybrid-electric bus. The model was developed in the MATLAB Simulink modelling environment. The validation algorithm is tested against vehicle dynamometer and test track data. With an increasing volume of mild and full hybrid vehicle configurations emerging, validation strategies such as the one proposed here are increasingly important for the design of energy management strategies to deliver the full potential benefits of the vehicle. The algorithm is proposed in a step by step method which can be automated to limit required user input.

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Optimisation of Low Temperature Combustion Technology, for Future Drive Cycles, using a Factorial Design of Experiments

Niekerk

Kay

Drew

et al. 2019

SAE Technical Paper Series

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Application of a Modular Simulation Approach: Optimizations from Combustion to Vehicle Management

Cited by 3 publications

References 8 publications

Investigation of an efficient model-based fuel economy optimization methodology for diesel engines focusing on real world driving emission cycles

Investigation of an efficient model-based fuel economy optimization methodology for diesel engines focusing on real world driving emission cycles

Multi-fidelity validation algorithm for next generation hybrid-electric vehicle system design

Optimisation of Low Temperature Combustion Technology, for Future Drive Cycles, using a Factorial Design of Experiments

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